Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

Abstract

This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-year period (1984 – 2017). Atmospheric forcing was provided by the Phase 2 North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark-beetle and wildfire disturbances. Annual summary values of simulated snowpack properties (snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation) were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade-1, and downward trends in winter precipitation were -52 mm decade-1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20 percent decade-1, peak SWE decreased by 14 percent decade-1, and total snowmelt quantity decreased by 13 percent decade-1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

“…Rather than reactively rescuing stranded fish during intermittency, conservation actions need to examine ways to limit the ultimate causes of streamflow intermittency in order to achieve recovery goals. Such proactive efforts face multiple challenges in the MRG, chiefly declining precipitation [23,94] and surface flows [25,95] coupled with over-appropriation of water [96]. However, proactive actions are likely more effective than reactive actions.…”

“…Indeed, the MRG fish assemblage formerly included species like shovelnose sturgeon Scaphirynchus platorhynchus and American eel Anguilla rostrata that would be intolerant of frequent drying [20]. More recently, frequent supra-seasonal drought [21], declines in snowpack [22,23], and human-mediated water abstraction of up to 95% [24,25] in the MRG Basin have resulted in long periods of summer streamflow intermittency, e.g., >100 days and >80 km in extreme years, averaging around 38 days and 35 km annually [18].…”

Trends and variability in snowmelt in China under climate change

“…(1) The model was performed on a monthly scale, whereas the output data were compared with the observed snow depth measured on the last day of each month, which increased the error. (2) The output data are at the grid scale, whereas the observed snow depth is at the site scale, where the snow properties are not always representative of snow at grid scale (Sexstone et al, 2020).…”

Trends and variability in snowmelt in China under climate change

“…(1) The model was performed on a monthly scale, whereas the output data were compared with the observed snow depth measured on the last day of each month, which increased the error. (2) The output data were at the grid scale, whereas the observed snow depth was at the site scale, where the snow properties were not always representative of snow at grid (Sexstone et al, 2020)…”